Perform is our company’s event once a year in Las Vegas, where our customers and partners visit us to learn more about our product and industry.

It was not my first time in Las Vegas. Therefore, the slot machines next to the baggage carousels and the bright lights that beamed into the night sky did not surprise me when I landed in this desert land’s organized chaos. However, it was my first time at Perform, and although I knew I would learn a thing or two in the next week, I was unaware of how beneficial taking part in this event would be.

Getting my hands dirty: Hands-on training at Perform

I was invited to join Perform to help host one of the HOT classes two days before the main conference event. These classes allow our customers and partners to learn more about specific areas of the Dynatrace product that interest them and their company. This year, the workshop-styled classes attracted nearly 1,500 attendees eager to learn more about “Power dashboarding,” “Advanced Diagnostics,” “Intro to DQL,” and a dozen other topics.

Over the two days, I helped run four HOT classes with Dirk Wallerstorfer, Radu Stefan, and Vagiz Duseev. Our classes were focused on building custom apps with AppEngine.

HOT class: Building apps with Dynatrace AppEngine

As this class name implies, we guided  attendees through the journey of building a Dynatrace app with a code-along example app. EasyTrade Analytics is a hypothetical stockbroker app developed by two members of the Platform enablement team, Sinisa Zubic and Edu Campver. The app displays account data of the virtual stockbroker “EasyTrade” and visually indicates if there have been any fraudulent activities on the accounts.

Our goal was that by the end of each HOT class, our attendees would feel confident to start brainstorming how they can use Dynatrace Apps to extend their current Dynatrace experience and to know where to go for all the resources they need to get started building their own apps.

While building the app with the attendees, we got a real sense of who our attendees were and what they wanted to achieve. And for me, it was the first time I had the opportunity to engage directly face-to-face with our customers, allowing me to put myself in their shoes and see their needs and struggles. This face-to-face interaction helped stimulate great conversation with the attendees and allowed us to go deeper into relevant topics of app development and help them get the most from the class.

Developer personas

To my surprise, I discovered that our attendees were a scattered mix across industries, with customers from airlines, healthcare, consulting, and insurance to supply chain, distribution, financial services, and likely more. This showed me how vast and versatile the Dynatrace product is and how it can be used for many use cases. And yet, our customers’ needs and wants for custom app development are quite similar.

There was one part of our class where we showed the attendees how to add annotations to the timeseries chart that will indicate the fraudulent activities in the EasyTrade analytics app. In each HOT class, this seemed to be the eye-opening moment for our attendees, where the app went from just a nice UX experience to a purposeful use case with something each of our attendees related to.

As expected, we had a wide range of coding abilities in each class, ranging from the advanced developer who finished building the app when we reached the second exercise to those who had recently started coding. One of my favorites was an attendee who had already tried to develop their app before the HOT class, having self-studied at a TypeScript and React crash course.

This proved to me that our assumption is correct: customers that want to create apps come with all types of experience in programming, reminding me of the importance of clear and well-structured documentation to ensure all levels can find the appropriate information when developing apps.

My takeaways from the HOT class

After completing the exercise, we encouraged our attendees to share what they would like to create using Dynatrace Apps. The attendees didn’t hold back and seemed more than happy to share their ideas, which caused a waterfall effect with every idea becoming more adventurous than the last; below are some of the ideas I took note of during the classes:

• An app for fraud alerts and alerts for fraudulent links.
• An app for tracking the custom user behavior of their customers.
• An app to create alerts that could be sent to their engineers.
• An app for helping diagnose bot traffic.
• An app with a highly customizable dashboard for a call center.

The big show starts

After the two days, with a mix of jet lag, early mornings, and extended teaching sessions–it didn’t stop!

In the following days, I helped manage the booth for Exploratory Analytics and Custom Solutions; this allowed me to discuss at length with our customers and many of the HOT class attendees what they wished to achieve when creating custom apps.

Additionally, I was able to sit in on many of the main stage events, learning more about the vision of Dynatrace from our CEO, Rick McConnell, and how Dynatrace faces many of the industry challenges from our CTO, Bernd Greifeneder. Even an exceptional performance from a local group of acrobats took the stage midway through the day!

Also, I sat in on one of our breakout sessions, hosted by Dirk Wallerstorfer and Paul Schumacher, who presented in a very comical way, “Dynatrace Apps: build your own in 10 minutes or less.” You can watch the recording from Perform on-demand.

Finally, I managed to squeeze in some time to record an interview for our newest episode of our YouTube series, Inside Dynatrace Apps, where I interviewed Dirk Wallerstorfer, a product manager at Dynatrace about the why and when you would want to create a Dynatrace App. You can watch the episode now on YouTube, by visiting Dynatrace Apps: Why and When.

From the HOT classes to the main conference, I would be lying if I told you I was not tired after the event. Although it left me feeling revitalized after the experience. Within a few days I had learned so much. Perform was a great event to widen my perspective, see first-hand from the customers, and learn more about the other areas of the Dynatrace product. And, of course, it’s nice to put names to the faces of many of my colleagues!

What you should do next

Are you interested in learning more about the work of us “Developer advocates” or do you want to share your personal experiences from this year’s Perform? Head over to the Dynatrace Community and let us hear your feedback or join our monthly office hours for Dynatrace App development and learn more from myself or my colleagues, Sarah and Sini.

The post Experiencing Perform: The diary of a Developer Advocate appeared first on Dynatrace news.

Overseeing cloud spend and IT resource allocation has always been a priority for CIOs. Yet, in 2023, 82% of cloud decision makers reported that managing cloud spend was their top challenge, according to one source. In response, many organizations are adopting a FinOps strategy.

FinOps is an evolving cloud financial management discipline focused on enabling organizations to get maximum business value from their cloud spend. Following FinOps practices, engineering, finance, and business teams take responsibility for their cloud usage, making data-driven spending decisions in a scalable and sustainable manner. FinOps aligns technology initiatives with business objectives while maintaining financial transparency and accountability to reduce unnecessary cloud spend and lower costs.

Empowering teams to manage their FinOps practices, however, requires teams to have access to reliable multicloud monitoring and analysis data. In a Dynatrace Perform 2024 session, Kristof Renders, director of innovation services, discussed how a stronger FinOps strategy coupled with observability can make a significant difference in helping teams to keep spiraling infrastructure costs under control and manage cloud spending.

Primary reasons for runaway cloud costs

Organizations are paying too much for too little return on their cloud investments for a few reasons, including the following:

Cloud waste. The primary factor that most IT and business professionals are aware of—but may not completely grasp—is how to control cloud waste. Teams provision and purchase many cloud services that end up underused or completely unused.

Wrong-sized resources. Aligning workload types and sizes with instance performance and capacity requirements is essential to keep costs down.

Unnecessary data transfer. Cloud vendors often charge data egress fees when data shifts from their platforms or between regions. For example, Amazon Web Services (AWS) charges for data transfer between Amazon EC2 instances within the same region.

On-demand payment agreement. On-demand payment is the most expensive pricing option. Flexible pricing models that offer discounts based on commitment or availability can greatly reduce cloud waste. This includes spot instances such as unused cloud capacity that’s available at a discounted price.

Suboptimal architecture design. Poorly designed cloud solutions can become costly over time. For example, poorly written code can consume a lot of resources, or an application can make unnecessary calls to cloud services.

How to implement an observability approach to FinOps to lower costs

An observability approach to FinOps involves applying financial management principles to monitoring and analyzing cloud resources and spending. It provides visibility, accountability, and optimization opportunities within the context of observability practices in cloud computing environments. This enables organizations to effectively manage cloud spend and drive cost optimization to achieve maximum ROI.

The following five practices are crucial to consider when implementing an observability approach to FinOps.

1. Cost allocation

The first step is to determine who—or which application—is spending what. Assign an owner—whether it’s a financial owner, business unit, or cost center—to each application. You can then start pulling that information into the observability platform. When each team’s cost is visible, it’s possible to identify who, what, or where the issue is if costs unexpectedly skyrocket. This visibility allows an organization to allocate costs and look at unallocated costs to drive optimizations when and where needed.

Are there rogue servers running in the environment where ITOps, CloudOps, or another team can’t assign or identify who’s financially responsible for it? This awareness is important when the goal is to drive cost-conscious engineering.

2. Proactive cost alerting

Proactive cost alerting is the practice of implementing automated systems or processes to monitor financial data, identify potential issues or anomalies, ensure compliance, and alert relevant stakeholders before problems escalate. Setting up and monitoring alerts for various metrics—such as resource usage, cost trends, budget thresholds, or deviations from expected spending patterns—can help FinOps teams stay ahead of unexpected expenses or budget overruns.

This proactive alerting involves combining many technologies that already exist in the Dynatrace platform. An organization can ask Dynatrace, “Have you seen any oversized servers over X amount of time?” Dynatrace automated intelligence Davis CoPilot can see all dependencies and identify those servers. You can then use that ownership capability to find out who this server belongs to and notify that person.

Hyperscaler cloud service providers such as AWS, Microsoft Azure, and Google Cloud Platform can do this, too. They can send a notification saying, “This server is oversized.” But Dynatrace goes further.

Dynatrace looks inside the machine and immediately tells you what’s running on it. So, the person who then receives the ticket also knows what’s running on that server.

3. Resource utilization monitoring

A FinOps observability approach involves monitoring cloud resource utilization to identify inefficiencies and optimization opportunities. Observability tools can provide insights into resource utilization metrics, such as CPU usage, memory usage, and network throughput. By analyzing these metrics in conjunction with cost data, organizations can identify underutilized resources that can be downsized or terminated to reduce costs.

4. Forecasting and budgeting

A FinOps strategy involves forecasting future cloud costs and budgeting accordingly to align spending with business goals and objectives. Observability tools can provide historical cost data and usage trends, enabling organizations to accurately forecast future spending. By incorporating financial forecasting into observability practices, organizations can proactively manage costs and avoid budget overruns.

5. Carbon impact monitoring

Carbon impact monitoring tracks, measures, and analyzes the carbon emissions of an organization’s activities. This concept is closely related to environmental sustainability and corporate responsibility, as organizations seek to minimize their carbon footprints and mitigate their effects on climate change.

Carbon impact optimization is similar to optimizing from a financial perspective. The Dynatrace Carbon Impact app delivers recommendations to enforce and validate sustainable engineering, such as oversized servers running on your infrastructure, or a server used for an underutilized application. It ties that cost and carbon growth to your business, using the six pillars of a well-architected framework as a guideline.

Drive your FinOps strategy with Dynatrace

In the simplest sense, FinOps is about optimizing and using cloud resources more efficiently. It’s about ensuring the cloud and IT resources you have provisioned are used completely and that your applications, transactions, and processes are using only necessary cloud computing resources.

Dynatrace can help you achieve your FinOps strategy using observability best practices. With critical insights into how much you’re spending and whether your costs are aligned with your business goals, you can optimize your environment across all clouds and make informed decisions to reduce cloud spend and your IT carbon footprint.

To learn more, watch the Dynatrace Perform 2024 breakout session, “Driving your FinOps strategy with Dynatrace.”

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In a digital-first world, site reliability engineers and IT data analysts face numerous challenges with data quality and reliability in their quest for cloud control. Increasingly, organizations seek to address these problems using AI techniques as part of their exploratory data analytics practices.

Exploratory data analytics is an analysis method that uses visualizations, including graphs and charts, to help IT teams investigate emerging data trends and circumvent issues, such as unexpected traffic spikes or performance degradations.

Challenges to exploratory data analytics

Among the challenges analysts face are multiple heterogeneous data sources, noisy or incomplete data, insufficient causal reasoning (faulty connections between event cause and effect), and untrustworthy AI, according to an article from the Columbia University Data Science Institute.

Another hurdle is mistaking easy patterns as effective analysis, according to an article in the Harvard Data Science Review. Techniques analysts use to emphasize patterns, such as aggregating data by default, can cause them to overlook variation and uncertainty in their data, so they can draw conclusions that the data don’t fully support.

AI techniques provide a solution

A first line of attack is selecting the right analytics tool, which can help teams detect meaningful patterns in real-time data, integrate data from multiple sources, and render data visualizations.

To that end, in 2022, Dynatrace released Grail, the auto-indexing, schema-on-read data lakehouse, along with Notebooks and Dashboards. This expansion of the Dynatrace platform builds on its foundation, which uses topology mapping and causal AI, an AI technique based on fault-tree analysis, to pinpoint root causes.

The next challenge is harnessing additional AI techniques to make exploratory data analytics even easier. Dynatrace Grail, Notebooks, Dashboards, and multiple AI techniques combine to provide analysts with instant insights, as demonstrated by Thomas Ziegelbecker, a senior product manager at Dynatrace, and his colleagues Peter Zahrer, principal product manager, and Gabriele Hasson-Birkenmayer, senior product manager at the recent Perform 2024 conference.

Three steps in exploratory data analytics: Discover, browse, explore

Grail captures heterogeneous data from across the network in one place while retaining its context and semantic details, which eliminates the limitations of traditional databases. From this unified, semantically rich data resource, analysts can explore data on the fly and share findings using Notebooks and Dashboards.

With Notebooks, analysts can “explore their data, use ad-hoc analysis, and work with data in a sequential fashion to refine, refine, refine,” Ziegelbecker said. “With Dashboards, you can observe [the data you’re interested in] over time,” covering common monitoring use cases such as system health.

“When you work with data, it comes down to three steps: Discover, browse, and explore,” Zielgelbecker said. “Start by asking yourself what’s there, whether it’s logs, metrics, or traces. Once you double down on the type, you want to figure out, or browse, which metric is relevant. Then when you have the metric, you want to explore it—what splits are available, how can I break it down, how can I aggregate it, and so on.”

Discover data using global search on the new ‘explore’ section and tile type in Notebooks and Dashboards

From a dashboard, an analyst can investigate an issue, such as a 25% error rate from a key Kubernetes cluster. According to Ziegelbecker, the discovery phase of exploratory data analytics can start in two ways: doing a global search or using the “explore data” interface.

1. Discovery using global search. Analysts can easily navigate to any entities (hosts, applications, processes, Kubernetes nodes, and so on) or metrics in their environment using global search. Users can trigger the global search from any context with CTRL/CMD +K. Type > to see a list of all available search categories. Select the relevant category (such as “Metrics”) and type the search term. This provides a quick way to navigate data or start a DQL query.
2. Discovery using the “explore data” interface. For those who think visually, Dynatrace provides an interface to explore data within Dashboards and Notebooks. Using step navigation and drop-down menus, this simplified UI approach streamlines query building for essential tasks such as filtering, aggregating, and sorting. This method also enables users to aggregate, filter, sort, limit, or split metrics.

Browse data: Advanced exploration using DQL

Once analysts discover the data they’re interested in, the next step is to refine the search and share results.

Dynatrace Notebooks is an interactive data exploration interface that enables users to collaborate using code, text, and rich media to build, evaluate, and share insights.

“[Notebooks] is purposely built to focus on data analytics,” Zahrer said. “We use Dashboards to monitor and present; we use Notebooks to work with the data and, at the same time, document what we just did.”

Using DQL, users can query any data stored in Grail, such as metrics, logs, events, and time series, in context of any entity (hosts, processes, applications) in the monitored environment, provided by Dynatrace Smartscape.

Using Notebooks, an analyst can extend the query started in the discovery phase to further troubleshoot an issue, such as a 25% error rate in a Kubernetes cluster. “I’m interested in seeing whether the [Kubernetes] log errors we’re seeing on our dashboard are related to a misconfiguration on the Kubernetes side,” Zahrer said.

To relate logs and metrics using Grail, analysts can use DQL within the Notebooks app to further explore the problematic Kubernetes logs. DQL prompts help analysts filter on enriched data from Dynatrace OneAgent—a single file that automatically discovers all the processes running on a host and auto-instruments application pages.

By enriching data with the topological context of Smartscape through OneAgent and keeping data consistent, Zahrer said, Dynatrace helps analysts do causation-based cross-correlation to see how events relate and to pass on details, such as Kubernetes nodes involved in errors, to further refine investigation and analysis.

Explore data using Davis CoPilot—a generative AI technique for advanced analytics of logs and metrics

As an alternative to identifying and exploring data, analysts can also use Davis CoPilot to achieve the same result.

“Davis CoPilot is a generative AI that works in concert with our predictive AI and with our causal AI,” Hasson-Birkenmayer said. The blend of these three AI techniques—predictive, causal, and generative AI—make up the Davis composite, or hypermodal, AI approach.

Davis CoPilot generative AI helps analysts get started with—and get more proficient using—DQL. Instead of configuring tiles or sections, or instead of writing DQL, analysts can explore data with a natural language prompt. For example, “you can ask Davis CoPilot to ‘summarize all logs by status,’” she explained. “In the background, Davis converts the prompt into DQL and auto-executes it. So you can go straight from a conversational prompt using your natural language directly into insights.”

Because the Davis CoPilot integration in Notebooks is a DQL assistant, it runs the query so analysts can see if the results are what they intended without having to first review and validate the DQL syntax themselves. If users need to refine the results, they can do so either by refining the natural language input or by creating a new DQL section and refining the details directly.

Analysts can also customize data visualizations according to their needs. “In some cases, I am actually choosing and selecting a particular visualization, and in some cases [such as with certain metrics], it gets done automatically,” Hasson-Birkenmayer said.

Exploratory data analytics enhanced by AI techniques

Starting from a dashboard or notebook using the “Explore data” interface to cover simple data exploration routines, analysts can advance their inquiries in Grail using DQL. With a combination of AI techniques—generative AI using data verified by predictive and causal AI—Davis CoPilot enables analysts to further refine complex explorations using natural language queries.

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Through containers developed within VA Platform One (VAPO), the development team at the U.S. Department of Veterans Affairs (VA) is packaging application code along with its libraries and dependencies within an executable software unit. The containers can run anywhere, whether a private data center, the public cloud or a developer’s own computing devices. Dynatrace container monitoring supports customers as they collect metrics, traces, logs, and other observability-enabled data to improve the health and performance of containerized applications.

At Perform 2024, Matthew Fuqua, Technical Lead for VAPuO, sat down with Willie Hicks, Dynatrace Public Sector Chief Technologist, to discuss his team’s role in VA’s modernization journey—and how Dynatrace has significantly accelerated its progress while unleashing new capabilities.

What is VAPO?

VA Platform One (VAPO) is a comprehensive application development and delivery platform. The VAPO platform is used to develop, test, and deploy containerized application and middleware workloads that support 400,000 VA employees and 20 million veterans. VAPO relies on Dynatrace and its integration with Red Hat to monitor application development and testing within containers to ensure optimal performance and security.

“It’s an enterprise product that we use to help modernize the VA,” Fuqua said. “It’s one of our biggest modernization efforts, and it’s saving us money while providing better, quicker, and faster healthcare to our veterans.”

VAPO is available in both Microsoft Azure and AWS. It’s supported by the VA Enterprise Cloud (VAEC), a multi-vendor, FedRAMP High environment for hosting VA applications in the cloud.

VA Platform One enables rapid application development with “security first” oversight

VA Platform One provides developers with all the tools required to create, test, and push out applications swiftly. With VA’s heavy focus on security, the platform enables developers to incorporate security testing for applications during development. “In the development environment, you see exactly where in the pipeline security issues exist, and you can address them right there, so it speeds up development,” Fuqua said. “It’s easy to produce a container that we can rapidly test, then shift to pre-production and production. It’s helping us build applications more efficiently and faster and get them in front of veterans.”

Supporting application modernization with a focus on user experience (UX)

While VA is developing new applications, they also have monolithic applications to modernize. That modernization effort starts with Dynatrace. “We can look at user experiences and CPU memory usage over time,” Fuqua said. “Then, we can show our results once we containerize: what we’re saving, if the user experience is better or worse and, if not, we can improve that. We use Dynatrace as part of our migration pattern, so we can see how well we’re doing our jobs.”

With Dynatrace, Fuqua’s team has full observability of the applications themselves in a dashboard, so his team can make sure users are getting all they need. “We can log into an actual experience and it captures everything,” he said.

AI and automation simplify container monitoring and satisfy the “need for speed”

Container monitoring is inherently challenging because of containers’ highly dynamic nature. Dynatrace artificial intelligence (AI)-powered root cause analysis brings real-time insights and actionable answers to fix issues, automating operations so the VAPO team can focus on innovation. “We want to get to a point where we can identify something, then the platform automatically creates a ticket that goes to an approver,” Fuqua said. “If the approver says, ‘do it,’ then it schedules the action.”

With a consistent focus on UX, VA is leveraging synthetic monitoring to gain an accurate view of UX and then using automation to scale containers ahead of demand. “We’re using automation to kick off scaling events,” he said. “We want to be there in time.”

The veteran is the mission

As Hicks summarized, VA’s mission is focused on the veteran. “Your most important end user is the veteran,” he said.

The VAPO team appreciates how Dynatrace puts everything “all in one place” while making it so much easier to solve problems. “This is a continuous process,” Fuqua said. “You’re not going to wave a magic wand and have a container. With Dynatrace, we’re getting in there and doing things in phases and continuously improving.”

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BizDevSecOps might sound like a mouthful, but it marks a necessary evolution. As business goals and technology efforts continue to converge, organizations need to ensure teams are performing to their full potential. Business considerations are now part of the security, operations, and development framework.

During a session at Dynatrace Perform 2024, Dynatrace colleagues Kristof Renders, director of innovation services, and Brian Chandler, principal solutions architect, demonstrated four BizDevSecOps use cases for the Dynatrace unified observability and security platform. Additionally, the pair illustrated the effect users can expect after implementation.

Getting granular with user experience

It all starts—and ends—with user experience. When users encounter issues with applications or services, performance and productivity drop. As a result, organizations need complete visibility into the user experience both individually and at scale.

The Dynatrace real-time user experience dashboard helps organizations discover where issues are happening and how they’re affecting users. “You can see where drop-off happens,” Chandler said. “You can see where people can do business KPIs [key performance indicators], and you can see where downticks happen. We’ve built the ability to track all business SLOs [service-level objectives].”

And with Dynatrace Site Reliability Guardian, all teams across the organization can understand how their specific silo operates in relation to critical systems.

Triaging BizDevSecOps problems using segmentation

Equipped with data that offers insight into the user experience, organizations are better prepared to triage potential problems using PurePath distributed traces. This starts with segmentation.

“You can segment by user session,” Chandler said. “This lets you jump right into triage. You can get an overview of the individual user—from what ISP they’re using, to where they’re connecting, to their screen resolution.”

These are all metrics Dynatrace collects directly out of the box. Organizations can also drill deeper to discover what’s happening on the server side.

“Dynatrace PurePath can trace hop to hop what went on in a user interaction to give a highly sophisticated root-cause analysis,” Chandler noted. “All of this data can be bubbled up to a unified dashboard.”

Users can then connect this dashboard data with underlying technical data, such as service-level agreement metrics.

Managing BizDevSecOps incidents quickly and effectively

With problems triaged and root causes identified, BizDevSecOps teams are ready for incident management. For Renders, the key to incident management is the ability to connect cause and effect: identifying what’s going wrong, why it’s going wrong, and where it started. In modern IT environments, however, creating these connections isn’t easy.

Where organizations used to have a half dozen legacy applications running on premises, they now have hundreds of local and cloud-based applications pulling data from different sources simultaneously. This creates complexity. While the direct effects of IT problems are obvious, the sources are often obscured.

“When something goes wrong, you want to get to a solution as quickly as possible,” Renders said. “Dynatrace will tell you that something is wrong and what is wrong. We can connect the root cause to the process owner.”

Deploying secure, well-architected applications

While many BizDevSecOps use cases center around identifying issues and mitigating their effect, Dynatrace can also help organizations ensure that application design, delivery, and deployment align with industry best practices, such as the six pillars of the AWS Well-Architected Framework.

“We can actually go and look at leveraging security information to stop badly performing apps from being released,” Renders noted. “Then, we can ask Dynatrace if an app is adhering to development pillars. We can go into our workflows and map out a well-architected application.”

For example, when a new app build is deployed and automated tests are executed, the outcome may trigger a quality gate. Dynatrace then performs automated quality validation through SLOs that either pass or fail the application and provide feedback to developers.

Unified observability is key to BizDevSecOps progress

From user experience to triage, incident management, and DevSecOps, Dynatrace delivers a unified observability and security platform that combines advanced AI and automation capabilities. The unified observability and security platform presents data in intuitive, user-friendly ways. This enables teams to gather and analyze data, while reducing mean time to repair and improving the performance and availability of applications. 

To learn more about how Dynatrace enables BizDevSecOps use cases, view the Perform session, “Top use cases for Biz, Dev, Sec, and Ops teams to get started with Dynatrace.” And for more information on news and insights from Perform, check out our guide.

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The growing complexity of modern multicloud environments has created a pressing need to converge observability and security analytics.

Security analytics is a discipline within IT security that focuses on proactive threat prevention using data analysis. As attackers become more skilled, threats can become more detrimental to organizations if they go undetected. A proactive approach to application security is essential: by constantly collecting and analyzing data, security analytics enables teams to catch problems before they escalate.

In the past five years, delivering innovation more efficiently has remained at the forefront of customer demand. As environments began to scale with cloud-native and microservices architectures to meet this demand, security approaches didn’t evolve accordingly. The result: Environments are more vulnerable to threats and more difficult to secure. In fact, according to a recent survey, nearly 70% of chief information security officers agreed that vulnerability management has become more difficult as the complexity of their software supply chain and cloud ecosystem has increased.

The ripple effect of increased risk compounds the problem. With more alerts and greater difficulty identifying false positives, security teams experience frustration and burnout. A lack of common tooling, common language, and collaboration further inhibits productivity and prolongs remediation.

At the 2024 Dynatrace Perform conference in Las Vegas, Gerhard Byrne, Dynatrace principal product manager, and Susan St. Clair, principal security solutions engineer, discussed how organizations can take a more proactive approach to threat detection and incident resolution.

During their breakout session, Byrne and St. Clair demonstrated how the Dynatrace platform accelerates remediation by enriching security data with observability context and actionable insights to protect environments against exploitation or lateral movement.

Threat hunting expectations vs. reality

In a perfect world, threat hunting and incident resolution would be a linear, straightforward process. Ideally, after fetching data and filtering, an organization could enrich the findings with observability data to get better insights into the nature of the alert. With these insights, the team could identify the threat and understand the nature of the incident. This allows them to react accordingly and return the system to a secure state.

But with the complexity of modern cloud environments — including the associated software supply chains and siloed toolchains — and the increasing sophistication of today’s attackers, threat hunting is unpredictable. In reality, security teams aren’t aware of all the unknown unknowns in their environments. After fetching, filtering, and enriching information with observability data, security teams might change their hypothesis about what’s occurring. This revision of assumptions might happen multiple times, causing engineers to lose track of previous hypotheses, patterns, and evidence. Keeping threats documented is a challenge: Engineers typically open numerous tabs to maintain context, which is tedious and can create error. Remediating a vulnerability can thus take far longer than anticipated, which can be detrimental when the risk is high.

“As defenders, we need to embrace different paths and possibilities like our adversaries are doing today,” Byrne said. “Just going down a checklist will not help you find new threats.”

Streamline threat hunting and accelerate resolution with Dynatrace Security Investigator

During the conference, Dynatrace announced the new Security Investigator app on the platform. The app enables security teams to investigate threats faster, obtain accurate and observability-enriched results, and maintain context throughout the weaving paths on which security investigations might lead.

Security Investigator demo

St. Clair began her demonstration of the app with the following scenario: She receives a Slack alert that an anomaly was detected and there has been unauthorized access to a Kubernetes cluster monitored via OneAgent.

To begin, St. Clair filtered the alert for a time window of a couple of hours to focus her analysis. Using Dynatrace Query Language in Grail, St. Clair determined what log data was available to her. With each execution, data appears in a query tree. “As I’m building out this investigation, each of these nodes is being created for me automatically,” she said. “As part of that documentation, I can easily go back and forth to see what was executed.”

St. Clair then used the Dynatrace Pattern Language (DPL) to make the data more usable. She used the DPL Architect to create her own patterns in addition to the platform’s out-of-the-box patterns for parsing the data. After running an audit log pattern, she wanted to understand why she received the Slack alert and which object the attacker had accessed so she could then focus on the unauthorized responses. Running this query, suspicious IPs arose, and she saved them as evidence in a new folder she created within the app.

As she continued to execute queries, St. Clair’s hypothesis began to change. She saw substantial traffic in a specific port, which was not necessarily malicious, but it was abnormal enough to warrant additional investigation. Fortunately, the query tree automatically creates new “branches” to support changing hypotheses and help engineers keep track of evidence and patterns. The query tree thus frees security professionals from tedious manual documentation, allowing them to focus entirely on finding the unknown unknown.

Finally, St. Clair found running malware on the system, pivoting the investigation from the initial authentication alert. By the end of an investigation, she had a visual representation of the process from start to finish.

“I can keep track of where I went. [The data is] documented, shareable, collaborative, and available for further investigation,” St. Clair said.

The road ahead: enriched security analytics with Dynatrace

Organizations can benefit most from their security investigations using the abundant data in the Dynatrace platform. The platform’s broad and deep observability identifies where problems initiate as well as their dependencies using PurePath. Teams can use Real User Monitoring and Session Replay to track user-facing activity in real time. This helps them understand how an attacker accessed an application, how they interacted with it, how traces originated, and more. Teams can also create management reports and use Dynatrace Notebooks to easily share their security investigation data with their peers.

“Security is a team sport,” Byrne said. “The next time you’re in a war room, you can be the person who provides insights and conclusions based on the wealth of data that is available at your fingertips with Dynatrace platform.”

The post Advanced security analytics to resolve incidents quickly and streamline threat hunting appeared first on Dynatrace news.

Today’s organizations flock to multicloud environments for myriad reasons, including increased scalability, agility, and performance. However, these environments can drown enterprises in data, forcing them to adopt multiple tools and services to manage and secure it. This fragmented approach adds complexity and opens the door to security vulnerabilities.

In fact, according to recent Dynatrace research, 85% of technology leaders say the number of tools, platforms, dashboards, and applications they use adds to the complexity of managing a multicloud environment. Further, 84% of technology leaders say multicloud complexity makes it harder to protect applications from security vulnerabilities and attacks.

With unified observability and security, organizations can protect their data and avoid tool sprawl with a single platform that delivers AI-driven analytics and intelligent automation.

During a Dynatrace Perform 2024 breakout session, Dynatrace colleagues Bipin Singh, product marketing director, and Markie Duby, principal solutions engineer, showed how organizations can bring together observability, security, and business data from cloud-native and multicloud environments with Dynatrace.

The secret sauce of unified observability

Observability enables teams to measure a system’s state based on the data it generates. A unified observability approach takes it a step further, enabling teams to monitor and secure their full stack on an AI-powered data platform.

With the Davis AI engine, Grail data lakehouse, and Smartscape topology visualization at its core, the Dynatrace unified observability and security platform provides AI-driven analytics and automation capabilities.

“Grail handles data storage, data management, and processes data at massive speed, scale, and cost efficiency,” Singh said. “Smartscape contextualizes your entire environment and builds a real-time topology map that’s dynamic and stays up to date as your environment changes. And the Davis AI engine is continuously watching your environment and evaluating the emerging situation, automatically detecting problems, creating automated root-cause analysis for you and business impact analysis for prioritization.”

The importance of hypermodal AI to unified observability

Artificial intelligence is a critical aspect of a unified observability strategy. In fact, according to the recent Dynatrace report, “The state of AI 2024,” 83% of technology leaders say AI has become mandatory to keep up with the growing complexity of multicloud environments.

The Davis AI engine uses a hypermodal approach to bring together causal, predictive, and generative AI. Causal AI determines the underlying causes and effects of issues based on the system’s topology. Predictive AI, meanwhile, makes predictions about future events based on patterns from historical data. And generative AI, termed Davis CoPilot, creates queries, notebooks, and dashboards to simplify analytics, and provides workflow and automation recommendations.

By bringing together these AI types, organizations receive generative AI recommendations based on the precise context from predictive and causal AI. This coactive AI approach enables organizations to spend more time on innovation by simplifying and automating routine tasks.

How Davis tackles root cause for AI-driven analytics

Duby discussed how Dynatrace OneAgent, Smartscape, and Davis work together to take information from many different layers in a full stack to provide root-cause analysis.

“When [Davis is] going through and detecting anomalies within your environment, it’s using data both from the underlying interdependency link as well as that end-to-end trace from the end user all the way back in order to do things like root-cause analysis,” Duby said. “We’re using that causal AI to determine what is actually the underlying root cause.”

Davis enables users to go deeper into the details of the underlying processes running on a particular host. The hypermodal AI engine shows what’s happening in a system down to the data coming in, while presenting the information in context.

“It’s one thing to have the data; it’s another thing to have it in context,” Duby continued. “For performance, for security analytics, you have to have the data in context. You need to understand how these different pieces interact with each other and how those pieces are actually coming through.”

Once Davis has gathered all the necessary information throughout the different layers of the stack, it can determine what’s changing, what’s breaking, where the issues are, and how to resolve them. Additionally, it helps users prioritize which issues need immediate attention by providing the necessary context.

“[Davis is] looking at the business context—not just the IT, not just the individual metrics, but understanding the whole picture,” Duby said.

How Davis CoPilot takes AI further and promotes collaboration

A significant piece of the Dynatrace hypermodal AI approach is Davis CoPilot, the generative AI part of the hypermodal engine. Davis CoPilot enables users to create queries, dashboards, and notebooks using natural language input, while offering coding suggestions for workflow automation. Additionally, it simplifies the processes of onboarding, configuring, and adopting the Dynatrace unified observability and security platform.

“This is Davis CoPilot. This is your helper to make sure you can actually go in and take advantage of all of that underlying data,” Duby said. “So, you have the analytics and the performance tracking that Dynatrace is doing, and you also have the ability to build it for your own custom use case.”

In addition to creating queries, dashboards, and notebooks using natural language, Davis CoPilot enables users to share these notebooks with other team members across the organization, boosting collaboration efforts.

“[Davis CoPilot will] start building out queries. And now I can take this information that I just got back, and I can share this notebook with my colleague. And now they have the exact same information,” Duby continued. “I can reuse the same report next month and see if it changed. … This functionality allows me to collaborate with my team. It allows me to run with a new idea and see what comes back. And then I can take this information, and I can build on top of it to do more advanced analytics for my different teams.”

For an in-depth demonstration of using the Dynatrace unified observability and security platform, watch our on-demand session, “AI-driven analytics and automation for unified observability and security.” And for more coverage from Perform 2024, check out our guide.

The post Unified observability delivers deeper insights with AI-driven analytics and automation appeared first on Dynatrace news.

When it comes to platform engineering, not only does observability play a vital role in the success of organizations’ transformation journeys—it’s key to successful platform engineering initiatives.

At the 2024 Dynatrace Perform conference in Las Vegas, Michael Winkler, senior principal product management at Dynatrace, ran a technical session exploring just some of the many ways in which Dynatrace helps to automate the processes around development, releases, and operation.

The various presenters in this session aligned platform engineering use cases with the software development lifecycle. Each use case provides its own unique value and impact, and whoever sees value in the use cases can adopt it—whether they are a platform engineer, DevOps engineer, performance engineer, or a site reliability engineer (SRE).

Check out the following use cases to learn how to drive innovation from development to production efficiently and securely with platform engineering observability.

Observability-driven development

Yarden Laifenfeld, senior software engineer at Dynatrace, presented the first use case for the software development lifecycle (SDLC): observability-driven development, which is the process of integrating observability and security before the first line of code is even written.

With observability-driven development, developers can understand what’s going on in their application in real time, early on. The whole organization benefits from consistency and governance across teams, projects, and throughout all stages of the development process.

Laifenfeld reviewed the benefits supported by observability-driven development with Dynatrace, including the following:

Templates for collaboration. With Dynatrace, organizations can easily define everything pertaining to observability, security, ownership, synthetic monitoring, and automation workflows using simple configuration files, which also ensure a persistent configuration state. Configuration as code is easy to use, update, and understand. Additionally, it can enable the creation of templates from these configurations, which teams can easily share between projects and services.

Standards for consistency. Organizations can then take these templates and standardize them. These standards can be custom for specific teams, technologies, or criticalities. Standards are set by the platform engineers and ensured throughout all stages of the software development lifecycle.

Real-time detection for fast remediation. Teams aspire to find and fix any issues as early in the software development lifecycle as possible by integrating observability and security capabilities. With observability, organizations can easily detect any performance bottlenecks, bugs, or security vulnerabilities earlier. And when issues are detected, the SLOs affected and the team with ownership over that service becomes visible, allowing for increased accountability and a faster fix time. Essentially, by bringing observability and transparency to developers, they’re able to gain real-time insights into what’s happening in their application and understand how their service is behaving in a real-life scenario.

Continuous testing validation

Gala Dvoretskaya, senior principal for digital experience at Dynatrace, discussed how observability enables continuous testing validation to support a seamless release process.

When organizations cross-validate with observability, security, and synthetic data, evaluation times shrink from days to minutes. Development teams can reference the following use cases to understand the benefits of observability-driven software releases with Dynatrace:

Easily consolidate and validate test results across tools. Using Dynatrace Workflows, users can execute a variety of different tests after the successful deployment of new releases.

Ensure seamless user journeys in every new release. Developers can validate the quality of their releases and ensure that user journeys meet customer expectations.

Automatically analyze and debug releases. Using Dynatrace, teams can directly access their synthetic monitors and drill down into locations where, for example, execution failed because of local or global outages. Users can also pull up the execution history and compare it to the successful execution, isolating the problematic areas.

Progressive delivery

Next up, Adam Gardner, staff engineer at Dynatrace, talked about using observability for faster innovation in production to enhance new releases.

According to Gardner, teams gradually deliver software to user groups with progressive delivery. The goal is to use the stepped release to minimize risk and error exposure so customers won’t get hit by an issue in production.

When releasing into production, Gardner said it’s important to think beyond performance metrics. With Dynatrace, teams can focus on other data like potential risks or pull data from third parties and get the answers they need—not just the data. Organizations get end-to-end observability from code to production with the right data in context.

Pipeline observability

Gardner then reviewed the importance of pipeline observability. Out of the thousands of business-critical pipelines, organizations need real-time observability of those pipelines. And that’s where lifecycle events come in. By standardizing what pipelines do—a pipeline started, a pipeline finished, a pipeline failed–Dynatrace enables organizations to understand how, when, and where pipelines are being used, and where the bottlenecks are.

Observability for infrastructure

Next up, Guido Deinhammer, CPO for infrastructure observability at Dynatrace, dove into the use case for observability across infrastructure and talked through new application offerings from Dynatrace.

According to Deinhammer, once software is released into production, it’s usually deployed at a large scale—often in complex multicloud or hybrid setups. Because of this, it’s critical for organizations to have end-to-end visibility across on-premises, cloud, and hybrid deployments. He goes on to review the following newly launched capabilities from Dynatrace:

Infrastructure & Operations app. The new Dynatrace Infrastructure & Operations app provides ITOps and SRE teams with an up-to-date and comprehensive view of their monitored environments. The app offers a consolidated overview across data centers and all monitored hosts. The app helps users quickly identify areas that require attention and drill down to the host level, where all necessary information is provided to quickly address any issue.

Databases. The new observability app for databases from Dynatrace was built to cater to all requirements of database administrators (DBAs), from swiftly assessing database availability and performance to delving into architectural intricacies for efficient troubleshooting. It also provides a single source of truth for all involved stakeholders and elevates problem resolution by uniting DBAs, application owners, and ITOps through a single, intuitive interface.

Dynatrace® Clouds app. The new Dynatrace Clouds app enables seamless management of multicloud environments and provides insights across multiple cloud services in a single, integrated view. It provides a cross-cloud overview of cloud services, their instances, and health, enabling cloud resource usage analysis and optimization with analytics notebooks. It helps teams to find under or overutilized resources and components quickly.

In addition to these new apps, Deinhammer discussed how Dynatrace Log insights and out-of-the-box dashboards and notebooks can help give organizations a complete picture of how their infrastructure is operating.

Kubernetes observability

Kubernetes infrastructure has many benefits, but it can also pose some complications. Florian Ortner, chief product officer at Dynatrace, explained how Dynatrace experienced this firsthand.

“We had many platform engineering practices built on top of Kubernetes,” Ortner said. “We suddenly had dozens, and then, later, hundreds of Kubernetes clusters running across the three big hyperscalers, and due to speed and cost reasons within our own engineering lab.”

Ortner reviewed the process of solving these issues.

“We discovered that it is crucial to not only manage the Kubernetes on our platform from a Kubernetes standpoint, but we must provide an out-of-the-box experience to our engineers,” Ortner said. “This is so they see all the things they are used to from open source tools from other solutions, and from kubectl right within Dynatrace. So, they see clusters, pods, and workloads for up to hundreds of Kubernetes clusters across AWS, Azure, and GCP—right within the new Kubernetes app.”

Kubernetes doesn’t have to be intimidating, especially with the right insight. The new Dynatrace Kubernetes experience enables platform engineers and SREs to better understand and optimize the health and performance of their Kubernetes environments.

Building a better development experience with observability

To close out the session, Winkler returned to the stage to drive home that observability and security will differentiate successful platform engineering initiatives from the others.

With full visibility into all the moving parts from development to production, organizations can reduce the cognitive load and increase developer experience for development teams.

The post Automating Success: Building a better developer experience with platform engineering appeared first on Dynatrace news.

Companies now recognize that technologies such as AI and cloud services have become mandatory to compete successfully.

AI data analysis can help development teams release software faster and at higher quality. AI-enabled chatbots can help service teams triage customer issues more efficiently. And security teams can use AI to proactively address potential threats to their IT environments.

According to the recent Dynatrace report, “The state of AI 2024,” 83% of technology leaders said AI has become mandatory to keep up with the dynamic nature of cloud environments. And according to an IDC report, organizations can now realize a return on AI investments within 14 months.

At the same time, the Dynatrace report revealed that 98% of technology leaders are concerned that some AI could be susceptible to unintentional bias, error, and misinformation.

So how can organizations ensure data quality, reliability, and freshness for AI-driven answers and insights? And how can they take advantage of AI without incurring skyrocketing costs to store, manage, and query data?

These are the goals of AI observability and data observability, a key theme at Dynatrace Perform 2024, the observability provider’s annual conference, which took place in Las Vegas from January 29 to February 1, 2024.

AI observability and data observability

The importance of effective AI data analysis to organizational success places a burden on leaders to better ensure that the data on which algorithms are based is accurate, timely, and unbiased. Increasingly, this focus on data quality will push organizations to adopt data observability technologies, which help organizations to determine the quality of their data.

But organizations also need to balance increasing AI adoption with the risks of runaway costs associated with increasing adoption.

Enter AI observability, which uses AI to understand the performance and cost-effectiveness details of various systems in an IT environment. As organizations adopt more AI technologies, the associated costs are skyrocketing. A key theme at Dynatrace Perform 2024 is the need for AI observability and AI data analysis to minimize the potential for skyrocketing AI costs.

‘Composite’ AI, platform engineering, AI data analysis through custom apps

This focus on data reliability and data quality also highlights the need for organizations to bring a “composite AI” approach to IT operations, security, and DevOps. A composite approach combines predictive, causal, and generative AI to ensure better data reliability. Dynatrace hypermodal AI is a specialization of composite AI for observability, security, and business analytics and automation. As a key component of hypermodal AI, causal AI is critical to feed quality data inputs to the algorithms that underpin generative AI.

This composite approach is also driving organizations to seek a unified observability platform that provides contextualized, centralized data in real time.

Another key theme at Dynatrace Perform 2024 is organizations’ growing adoption of platform engineering, which helps accelerate the delivery of software applications. Platform engineering improves developer productivity by providing self-service capabilities with automated infrastructure operations. How organizations use methodologies such as platform engineering may determine organizations’ success or failure in the year to come.

Speakers at Dynatrace Perform 2024 will also explore how organizations can garner business value from their data by building custom applications that serve core organizational needs. Once organizations can unify their data in a trusted cloud observability platform, they can act on—and trust—the insights they gather. In turn, organizations have the tools to build secure, compliant custom apps that serve their business needs and fit easily into their larger multicloud ecosystems.

In what follows, we explore these key cloud observability trends in 2024. Join us at Dynatrace Perform 2024, either on-site or virtually, to explore these themes further.

Dynatrace Perform 2024 news

At Dynatrace Perform 2024 in Las Vegas, the headliner theme is AI-enabled data. Check back here throughout the event for the latest news, insights, and announcements.

	The benefits of unified observability and security for BizDevSecOps use cases – blog During a Dynatrace Perform 2024 session, experts demonstrated how unified observability and security can benefit BizDevSecOps use cases.
	Unified observability is key to consolidating tool sprawl and breaking down data silos – blog Discover how to consolidate tool sprawl and break down data silos with unified observability.
	Unified observability delivers deeper insights with AI-driven analytics and automation – blog Discover the importance of unified observability, AI-driven analytics, and intelligent automation to get the most value from your data.
	Automating Success: Building a better developer experience with platform engineering – blog Dynatrace supports platform engineering initiatives, improves developer productivity, and helps teams build and operate software better.
	The future of work: How to zig, zag, and steer your career in the AI era – blog The ‘Women in Technology’ panelists at Dynatrace Perform 2024 discussed embracing change and continuous learning —key strategies for the future of work in the AI era.
	Cloud observability now mandatory for organizations to thrive amid digital disruption – blog At Dynatrace Perform 2024, CEO Rick McConnell said that cloud observability and AI-powered strategies are now essential for organizations to compete amid dynamic, disruptive macroenvironments.
	Mitigating risk with AI observability: Dynatrace empowers organizations to embrace AI for all use cases – blog At Dynatrace Perform 2024, Bernd Greifeneder and Alois Reitbauer discuss AI observability and how organizations can embrace AI properly.
	Cloud cost optimization: Dynatrace helps organizations manage cloud cost – blog Cloud cost optimization and managing cloud costs are major priorities for every industry to save money and reduce cloud carbon footprint.
	Trace, diagnose, resolve: Introducing the Infrastructure & Operations app for streamlined troubleshooting – product news The new Dynatrace Infrastructure & Operations app provides ITOps and SRE teams with an up-to-date and comprehensive view of their monitored environments.
	Dynatrace launches Databases app to provide DBA insights across all databases – product news Introducing Databases, the new observability app for databases from Dynatrace.
	Speed up evidence-driven security investigations and threat hunting with Dynatrace Security Investigator – product news Dynatrace Security Investigator is a new application on the Dynatrace platform dedicated to security operations and security analysts.
	Dynatrace launches Databases app to provide DBA insights across all databases – product news Introducing Databases, the new observability app for databases from Dynatrace.
	Observe and optimize multicloud environments with the Dynatrace Clouds app – product news The new Dynatrace® Clouds app enables seamless management of multicloud environments and provides insights across multiple cloud services in a single, integrated view.
	Kubernetes health at a glance: One experience to rule it all – product news  The new Dynatrace Kubernetes experience enables platform engineers and SREs to better understand and optimize the health and performance of their Kubernetes environments.
	Dynatrace extends AI-powered observability for SAP together with PowerConnect – product news Dynatrace further extends its capabilities to monitor SAP systems with PowerConnect for enhanced observability across SAP systems.
	Embrace enterprise-wide observability and security with Foundation & Discovery – product news Announcing Discovery & Coverage, a new app for the Dynatrace® platform, and a new OneAgent® mode called Foundation & Discovery.
	Dynatrace OpenPipeline: Stream processing data ingestion converges observability, security, and business data at massive scale for analytics and automation in context – product news Dynatrace addresses data challenges with a single, built-in data ingest functionality: Dynatrace OpenPipeline™, the ultimate addition for data-driven organizations.
	Dynatrace accelerates business transformation with new AI observability solution – product news Adoption of artificial intelligence (AI) is increasingly imperative for any organization that hopes to remain competitive in the future. However, the benefits of AI are not as straightforward as they might first appear.
	Introducing Dynatrace built-in data observability on Davis AI and Grail – product news Dynatrace now addresses many issues customers experience around the health, quality, freshness, and general usefulness of data externally sourced into Dynatrace Grail.
	Dynatrace Launches AI Observability for Large Language Models and Generative AI – press release Enables organizations to embrace AI with confidence by providing unparalleled insights into all layers of AI-powered applications, helping ensure security, reliability, performance, and cost-effectiveness
	Dynatrace Unveils Data Observability for its Analytics and Automation Platform – press release Davis AI helps ensure all data in the Dynatrace platform is reliable and accurate for business analytics, smart cloud orchestration, and reliable automation
	Dynatrace Releases OpenPipeline for its Analytics and Automation Platform – press release Enables full control of data at ingest and evaluates data streams five to ten times faster than legacy technologies, helping boost security, ease management, and maximize the value of data
	Dynatrace Teams with Lloyds Banking Group to Reduce IT Carbon Emissions – press release Real-time insights support leading financial institution to meet its sustainability goals
	Generative AI model observability, cloud modernization take center stage with partners at Dynatrace Perform 2024 – blog Cloud partners AWS, Azure, and GCP talk generative AI models, cloud modernization, and cloud migration at Dynatrace Perform 2024.

Deriving business value with AI, IT automation, and data reliability

When it comes to increasing business efficiency, boosting productivity, and speeding innovation, artificial intelligence takes center stage. In fact, according to the Dynatrace report, “The state of AI 2024,” nearly three-quarters of IT operations, development, and security teams plan to use AI to become more proactive in executing their work. Further, 62% of organizations have already changed the job roles and skills they are recruiting for to incorporate AI. Because of these trends, AI data analysis and IT automation are front and center at Perform 2024.

But for organizations to maximize the business benefits of AI, they need to continuously evaluate their data to ensure it’s high-quality data, which is the bedrock of solid decision making. This is especially true when taking a composite approach to AI, converging AI types such as causal and generative AI to ensure data reliability. For example, high-quality data and predictive AI enable causal AI to provide precise, continuous, and actionable insights in real time.

The following resources provide more information on how to get the most out of your AI investment, the importance of data quality for business success, and automating manual IT processes to prioritize innovation.

	Why growing AI adoption requires an AI observability strategy – blog While AI adoption brings operational efficiency and innovation for organizations, it also introduces the potential for runaway AI costs. How can organizations use AI observability to optimize AI costs?
	Responsible AI must-haves for unified observability and security – blog As organizations turn to AI, how can they ensure that the data and algorithms that fuel AI are based on trusted, unbiased, and responsible AI?
	The state of AI in 2024: Overcoming adoption challenges to unlock organizational success – blog In the “State of AI” report, respondents outlined the benefits and challenges of AI.
	What is causal AI? Why this deterministic AI approach is critical to business success – blog Today’s organizations need to go beyond a traditional, correlation-driven approach to identify the underlying causes and effects of an event or behavior and drive better DevOps automation. Enter causal AI.
	Measuring the importance of data quality to causal AI success – blog Causal AI can accurately pinpoint why an event occurred, but the effectiveness of AI depends on high-quality data. Discover common data quality challenges, how to improve data quality, and more.

Cloud observability is central to platform engineering

The uptick in digital transformation initiatives has created a drive for scalability among global organizations. But the demand for faster delivery speeds and higher-quality software has demonstrated that current software delivery methods are no longer sufficient. Teams face siloed processes and toolsets, vast volumes of data, and redundant manual tasks. To release software at the speed and quality that customers demand, organizations have begun prioritizing automation and creating self-service capabilities, also known as platform engineering.

Platform engineering involves building internal platforms to provide a self-service library to software developers. The goal of the practice is to reduce manual effort and redundant tasks to allow developers to spend more time innovating. The discipline shows promise: According to Gartner, 80% of software engineering organizations “will establish platform teams as internal providers of reusable services, components, and tools for application delivery” by 2026. Recent research also found that 54% of organizations are investing in platforms to enable easier tool integration and collaboration between teams involved in automation projects.

But to achieve the operational efficiency, agility, and optimized developer experience that platform engineering stands to bring, organizations need cloud observability and AI data analysis integrated into their platforms. Building observability-as-code into platform engineering enables automatic service-level objective creation, defined ownership, enriched context, and problem routing to ensure platforms remain available and reliable for developers.

To learn more about platform engineering, explore the following resources.

	Unlock the Power of DevSecOps with Newly Released Kubernetes Experience for Platform Engineering – blog The development of internal platform teams has taken off in the last three years, primarily in response to the challenges inherent in scaling modern, containerized IT infrastructures.
	What is platform engineering? – blog Platform engineering enables development teams to deliver frictionless, self-service developer experience with minimum overhead. Learn the importance of platform engineers and more.
	Platform engineering: Empowering key Kubernetes use cases with Dynatrace – blog Digital transformation continues surging forward. Today, speed and DevOps automation are critical to innovating faster, and platform engineering has emerged as an answer to some of the most significant challenges DevOps teams are facing.
	The platform engineer role: A game-changer or just hype? – blog The platform engineer role is gaining speed as the newest byproduct of scaling DevOps in the emerging but complex cloud-native world. What is this new discipline, and is it a game-changer or just hype?
	The Observability Guide to Platform Engineering  – Part 1: Platform Observability & Success KPIs – webinar Observability is needed to understand whether the product works as expected, is efficient, is resilient, and provides the desired value to the end users. Join this Observability Clinic to learn more.

Custom apps deliver value for key business needs

More organizations are increasing their reliance on cloud-based technologies. In fact, Gartner forecasts that spending on public cloud services will total $679 billion in 2024 and exceed $1 trillion by 2027. In 2023, organizations mostly used these services as a technology disruptor and capability enabler. But by 2028, these cloud-based services will be a business necessity.

Although the business case for cloud-native technologies is clear, they often require a complex mix of multicloud, hybrid cloud, and on-premises environments. Organizations increasingly struggle with the challenge of monitoring the explosion of microservices and tools that come with these environments.

While app-centric serverless approaches abstract some of the complexities of cloud-native architecture, as the analyst firm Forrester notes, the next frontier for serverless adoption is at the edge. Edge computing brings compute and data storage closer to where data is generated to help reduce costs, boost performance, and improve customer experience.

Organizations are also turning to AI data analysis to enable cloud cost efficiency through FinOps, and to address threat detection, operations automation, and deployment validation use cases. As organizations adopt large language models and generative AI technologies to accelerate efficiency, they introduce yet another dimension of complexity—including security and energy consumption concerns—that needs monitoring.

With this myriad of concerns, organizations need an automated, AI-driven, observability platform approach that can run specialized analysis on a massive scale through custom apps. When accessing observability data for every use case from a schemaless, indexless data lakehouse, out-of-the-box apps provide instant business-critical analysis. And the ability to easily create custom apps enables teams to do any analytics at any time for any use case.

Learn more about what kinds of business questions custom apps can answer from the following resources.

	Dynatrace and Red Hat expand enterprise observability to edge computing – blog Cloud-native workloads at the edge save costs and boost performance. However, edge observability can be challenging due to distribution, resource limits, and security issues. Continue reading to learn more.
	Sustainable IT: Optimize your hybrid-cloud carbon footprint – blog As global warming increases, growing IT carbon footprints make energy-efficient, carbon-optimized computing a top priority for many organizations.
	What is FinOps? How to keep cloud spend in check – blog As cloud spend continues to reach new heights, organizations need a new approach to keep costs in check. Enter FinOps, a public cloud management philosophy that aims to control costs.
	Automated Change Impact Analysis with Site Reliability Guardian – blog The Dynatrace® Site Reliability Guardian simplifies the adoption of DevOps and SRE best practices to ensure reliable, secure, and high-quality releases.
	Improving customer experience with business process monitoring – blog Monitoring business processes is one thing organizations can do to help improve the key business processes that enable them to provide great customer experiences.
	AppEngine empowers organizations to create custom apps for better data insights – blog Learn more about creating custom apps for better data insights.
	Start strong: Words of wisdom for creating Dynatrace Apps – blog With the release of Dynatrace AppEngine, we revealed how to create your own custom Dynatrace® Apps.

Unified observability and security for business value

As organizations increasingly rely on AI, automation, and cloud operations, data observability and security have never been more vital to business success. As the volume of data grows, organizations urgently seek to ingest and analyze it faster and at a greater scale. However, the costs and risks of poor observability and security of that data are greater than ever. As a result, organizations will increasingly require data observability to enable the rapid and secure ingestion of high-quality and reliable data that is ready to use.

Unified data observability and security are essential to generating insights that users can trust by ensuring the freshness of data, identifying anomalies, and remediating errors. It also supports responsible and accurate AI data analysis, ensures that organizations have the tools to build secure, compliant custom apps, and enables efficient automation, allowing organizations to do more with less—a fast-growing requirement.

Check out the resources below to learn more about unified observability and security for achieving business value.

	Technology predictions for 2024: Dynatrace expectations for observability, security, and AI trends – blog In our 2024 technology predictions roundup, we explore our expectations for key technologies, such as digital immune systems, generative AI, and more.
	Cloud observability delivers on business value – blog Cloud observability enables organizations to deliver business value by reducing costs, minimizing IT incidents, and providing better user experiences, as CEO Rick McConnell outlined at the recent Innovate conference.
	What is data observability? – knowledge base Learn how data observability can help identify, alert, troubleshoot, and resolve data issues in real time.
	Achieving business resilience with modern observability, AI, and automation – blog Organizations need a technology foundation that promotes business resilience, agility, and flexibility.
	Global Report Reveals DevOps Automation is Becoming a Strategic Imperative for Large Organizations, but Only 38% Have a Clear Strategy for Implementing It – press release Automation is helping teams improve software quality and reduce costs, yet organizations have only automated 56% of their DevOps lifecycle
	Security by design enhanced by unified observability and security – blog With Dynatrace, Soldo teams can see what’s happening in a cluster and also correlate among all the applications and workloads. This includes the Kubernetes cluster itself and all the other elements running in their IT environment.
	Dynatrace Grail: The data lakehouse for observability and security analysis and automation – blog Organizations need an effective way to store, contextualize, and query data to get immediate insights and drive automation.

Join us for Dynatrace Perform 2024.

Ready to try Dynatrace for free? Learn more about our trial.

The post Dynatrace Perform 2024 Guide: Deriving business value from AI data analysis appeared first on Dynatrace news.

Every year at our annual user conference, Dynatrace Perform, we recognize the most inspiring success stories from our most innovative, transformative customers and partners.

At Dynatrace Perform 2024, we had the honor of again hosting our awards ceremony to publicly acknowledge the organizations that use observability, AI, and analytics to manage modern cloud complexity, secure their business, and drive meaningful impact across their industries. We’re proud to announce the following winners:

These awards recognize the industry game changers whose outstanding contributions have helped push the boundaries of software intelligence. Dynatrace shares these stories to inspire innovation, empower change, and enable the confidence necessary for organizations to accelerate their digital transformation.

Congratulations to the winners!

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Business and technology leaders are increasing their investments in AI to achieve business goals and improve operational efficiency. From generating new code and boosting developer productivity to finding the root cause of performance issues with ease, the benefits of AI are numerous. However, without observability, the AI wave—which has become more than just hype—comes with risks.

The first risk is not adopting AI at all. Organizations that miss out on implementing AI risk falling behind their competition in an age where software delivery speed, agility, and security are crucial success factors. But organizations must also be aware of the pitfalls of AI: security and compliance risks, biases, misinformation, and lack of insight into critical metrics (including availability, code development, infrastructure, databases, and more).

At the 2024 Dynatrace Perform conference in Las Vegas, Dynatrace chief technology strategist Alois Reitbauer joined founder and chief technology officer Bernd Greifeneder to discuss how organizations can mitigate their risk and embrace AI properly. Reitbauer delved into key use cases for Dynatrace hypermodal AI before Greifeneder introduced the platform’s new AI observability capability that will revolutionize how organizations can maximize value from their AI implementations.

Expanding hypermodal AI for all use cases with Davis CoPilot™

The Dynatrace composite approach to AI, known as hypermodal AI, enables organizations to extract the maximum ROI from their data efficiently and cost-effectively. Hypermodal AI combines predictive, causal, and generative AI so that organizations can not only predict behavior and identify root causes but also effortlessly store, query, and access their data using natural language and at a massive scale.

Moreover, with Davis CoPilot™, organizations can use generative AI to harness the full extent of hypermodal AI for every use case. “Every new question we ask comes with additional analysis, prediction models, and more,” Reitbauer said. “By packaging [these capabilities] into hypermodal AI, we are able to run deep custom analytics use cases in sixty seconds or less.”

Performance analytics

Dynatrace hypermodal AI empowers development teams to dig deep into database statements and remediate issues quickly. The first use case Reitbauer presented outlines a developer who hears from the database team that a problem might be occurring with the database. The developer opens a Dynatrace notebook, creates a CoPilot section, and asks for all the database statements executed over the past 72 hours using a natural language query.

In response, CoPilot delivers the data showing the variety of statements executed over the given timeframe. Armed with this data, the developer can then change the query to ask for the statements over a period (for example, the previous week). CoPilot then delivers the data, enabling the developer to easily identify regressions, increases, and more.

Security analytics

Davis CoPilot proactively mitigates risk by enabling security teams to validate hypotheses and hunt for threats before they impact the organization or end users. The second use case involves a security engineer who becomes aware of a new threat and wants to know if any of the organization’s systems might be affected. The engineer can efficiently access this data via a natural language query in a CoPilot Notebook: “Summarize all MITRE security events of the last 72 hours.”

In this example, there is a suspicious increase in scripting events. The key advantage of hypermodality is that users can frictionlessly switch from business events to logs to drill down further into their data. After updating the query to ask for log data, the engineer was able to identify attack attempts.

Experience analytics

Hypermodal AI also helps teams analyze issue reports, write scripts, and pinpoint root cause to maintain superior customer experiences. In the third use case, site reliability engineers (SREs) use Davis CoPilot to examine the number of problems over time to understand user impact. An SRE can translate a DQL query from a colleague into natural language, modify it, and examine the result. CoPilot efficiently provides valuable problem data so the SRE can then collaborate with the customer experience team to remediate the issue.

FinOps

The fourth use case illustrates how FinOps can also benefit from hypermodal AI. With as a significant priority for business and technology leaders, FinOps engineers can use CoPilot to understand the number of nodes on a Kubernetes cluster. Generative AI partners with predictive AI to deliver insights into the number of nodes for a specific cluster they will need in the future, thus improving efficiency while ensuring a frictionless customer experience.

Business workloads

The final use case considers an e-commerce business whose analyst team wants to understand the minimum daily number of orders they need to fulfill, in addition to predicting how much packaging they will need for an average week. First, they import their order data into the Dynatrace platform using the business events capability. From there, they can use predictive AI to forecast their daily workload. They can also modify the query to view their predicted workload on a weekly basis, forecast order amounts, and efficiently plan their packaging.

Mitigate risk for all use cases with AI observability

Reitbauer’s exploration of hypermodal AI and its variety of applicable use cases demonstrated that AI can deliver exponential value. As organizations consider the next AI services they will build, visibility into their implementations is critical to ensuring success.

At Perform 2024, Dynatrace announced the immediate availability of AI Observability, a capability that enables organizations to observe how their AI implementations are behaving. The complexity of modern cloud environments necessitates broad and deep observability into every layer of the tech stack to mitigate risk and deliver software quickly and securely. AI implementations are no exception.

AI Observability provides insights into all layers of the tech stack for which organizations might be using AI, including infrastructure, GPUs, semantics, vector databases, orchestration, and more. This also comes with context into the rest of your stack, with 700+ integrations out of the box: “[With AI observability,] you can observe not only the AI itself, but the applications and services that you bring to your customers,” Greifeneder said.

But contextual analytics don’t stop here. “AI Observability brings the ROI back to the business,” Greifeneder continued. “You can figure out if [your AI implementations] are bringing the value, customer experience, performance, and efficiency you need.”

AI observability is also a critical capability because of the increasing risk of duplicated code that comes with generative AI implementations. “Generated code becomes less maintainable. It poses more security risks due to its convenience,” said Greifeneder. “Dynatrace observes these applications that have been created with or augmented by generated code.” The Dynatrace composite approach to AI thus keeps applications secure while allowing organizations to reap the benefits of artificial intelligence in a variety of use cases.

Embrace AI completely, properly, and confidently with AI observability

As organizations navigate AI and explore how they can use the technology for their unique business goals, the Dynatrace platform empowers organizations to embrace AI properly. With its combination of predictive, causal, and generative AI, Dynatrace hypermodal AI enables cost-effective and efficient processes that promote collaboration, swift issue resolution before they reach end users, and the delivery of superior customer experiences.

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Business innovation costs money. But organizations may not always have insight into how their innovation initiatives generate costs as well as revenue. That’s why cloud cost optimization is becoming a major priority regardless of where organizations are on their digital transformation journeys.

From managing cloud cost with the major providers to the increasing compute costs that generative AI and large language models (LLMs) create—and the carbon footprint they consume—the cost of innovation is affecting bottom lines across the industry.

In fact, Gartner’s 2023 forecast is for worldwide public cloud spending to reach nearly $600 billion. To put that into perspective, analysts at venture capital firm Andreessen Horowitz report that many companies spend more than 80% of their total capital raised on compute resources.

Generative AI and LLMs are compounding these figures. For example, a CNBC report found that training just one LLM can cost millions, then millions more to update.

These costs also have an environmental impact. A Cloud Carbon Footprint report found that global greenhouse gas emissions from technology rival or exceed the aviation industry.

Bernd Greifeneder, Dynatrace founder and CTO, acknowledged at Dynatrace Perform 2024 that even his team suffers from tool sprawl.

“CNCFs, thousands of tools, they pick up everything,” Greifeneder said. A platform like Dynatrace with Grail data lakehouse and AppEngine helps teams monitor and manage cloud costs associated with these tools and services. “We had many do-it-yourself tools that integrated with security and did some other import tasks,” Greifeneder said. “We shifted all that onto the [Dynatrace] platform so we have compliance and privacy issues solved.”

The cost of tool sprawl

The cost of tool sprawl is not just the tool itself. Maintaining consistency, updates, and collaboration among the data and the teams that use the various tools means they waste time building and maintaining integrations while potentially losing important data and its context.

“Every dollar we spend on cloud [infrastructure] is a dollar less we can spend on innovation and customer experience,” said Matthias Dollentz-Scharer, Dynatrace chief customer officer. Dollentz-Scharer outlined four phases of cloud cost optimization and how Dynatrace helps with each.

• Financial. Dynatrace enhances platform optimizations negotiated with cloud providers by automating savings plans and cost allocations and giving teams insights into how they use cloud resources.
• Utilization. By tracking over- and under-utilized machines, Dynatrace helps teams track underutilized machines and minimize overprovisioning.
• Architecture. The Dynatrace platform tracks workload and dependencies among resources. With its topology mapping and dependency tracking, Dynatrace provides process-level tools that help determine which processes use what resources to aid with troubleshooting and optimization.
• Smart orchestration. By ensuring automated workloads run most cost-effectively, Dynatrace monitors automated workloads, such as in Kubernetes environments, to help teams eliminate redundant services for further cost savings.

How cloud cost optimization mitigates the effects of tool sprawl

For example, the Dynatrace team investigated its Amazon Elastic Block Store (EBS) usage. To discover why EBS usage was growing in relation to the Dynatrace architecture, the team used Dynatrace Query Language (DQL) and instrumentation notebooks. These tools helped them determine what processes were using the resources. “The team tweaked the Dynatrace [AWS] deployment and automated discrete sizings in Kafka, which helped us save a couple of million dollars,” Dollentz-Scharer said.

“When we started deploying Grail in several hyperscaler locations, we were able to use more predictive AI from our Davis AI capabilities to make our orchestration even smarter,” Dollentz-Scharer continued. That enabled the Dynatrace team to reduce its EBS usage by 50%.

Likewise, when an automation glitch deployed a batch of unneeded Kubernetes workloads into a new Grail region, Dynatrace instrumentation and tooling surfaced the problem. The team fixed it within 48 hours, enabling them to manage cloud costs and save resources.

Reducing cloud carbon footprint

A driving motivation for cloud cost optimization and resource utilization awareness is climate change. The effects of global climate change are evident everywhere, from wildfires to hurricanes to catastrophic flooding.

As a result, Dynatrace introduced the Carbon Impact app in 2023. With its unique vantage point over the entire multicloud landscape, its workloads, and interdependencies, Dynatrace helps organizations pinpoint and optimize their carbon usage with precision.

“Everyone has a part to play,” said a representative of a major banking group. “Our commitment is to achieve net 0 carbon operations and reduce our direct carbon emissions by at least 75%, and reduce our total energy consumption by 50%, all by 2030.” The organization has already met its commitment to switch to 100% renewable energy.

The company’s IT ecosystem uses thousands of services across traditional data centers and hybrid cloud environments, and it’s continually growing. Accordingly, he said, “it’s critical that we have observability of our energy consumption and footprint to monitor the impact of this growth over time.”

You can’t manage what you can’t measure

The organization uses Dynatrace to optimize carbon consumption at the data center, host, and application levels. As a result, they’re modernizing data centers from the ground up with sustainability and efficiency in mind and identifying underutilized infrastructure.

“But it’s very important as we do this to find that sweet spot,” the banking group representative said. “We can’t risk the stability or performance of the services.” Critically, Dynatrace helps the group’s team observe how reducing energy consumption relates to resilience.

Implementing “Green-coding principles”

At the application level, the banking group is now applying green coding, a practice that minimizes compute energy consumption. Green coding enables them to optimize and analyze application source code to run more energy-efficient CPU cycles and memory utilization.

“We’ll be introducing quality gates for software and development and testing stages to ensure that any new code is as efficient as possible.” This includes writing code in the most efficient languages for each use case. Dynatrace is a key enabler of the group’s approach to compare a baseline CO₂ measurement of their application code before and after each change.

The organization’s proof of concept, a single application programming interface (API), yielded a reduction of around 2 tons of CO₂ per year. This promises to yield significant savings when applied to all their applications.

End-to-end observability makes it possible to measure both performance and carbon consumption and to manage cloud costs. “In performance optimization, you’re hunting down milliseconds, and with carbon optimization, you’re hunting down grams,” Dollentz-Scharer observed.

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With our annual user conference, Dynatrace Perform 2024 rapidly approaching on January 29 through February 1, 2024, our teams, partners, and customers are buzzing with excitement and anticipation. Perform serves yearly as the marquis Dynatrace event to unveil new announcements, learn about new uses and best practices, and meet with peers and partners alike. At this year’s Perform, we are thrilled to have our three strategic cloud partners, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), returning as both sponsors and presenters to share their expertise about cloud modernization and observability of generative AI models.

Dynatrace unified observability and security is critical to not only keeping systems high performing and risk-free, but also to accelerating customer migration, adoption, and efficient usage of their cloud of choice. More so than ever before, organizations are investing in cloud migration and cloud modernization to lower total cost of ownership (TCO). These investments, in turn, extend to unifying observability with context and intelligence across increasingly dynamic and complex cloud environments, and ensuring that their cloud ecosystems are not only reliable and secure but also optimized to realize resource savings and accelerate release delivery.

At this year’s Perform, all three of our cloud partners will take the stage to speak about these benefits—and how to achieve them—in their expert-led sessions. Takeaways will be immediately applicable for both technologists and business stakeholders alike, helping attendees put into place the right observability and security practices with Dynatrace to further advance their cloud modernization journey (and make it a smooth one at that).

Read on to learn what you can look forward to hearing about from each of our cloud partners at Perform. If you’re unable to join us in Las Vegas, be sure to register to attend virtually—or view sessions on-demand afterward—so you don’t miss out!

Accelerating AWS migration and optimizing efficiency with Dynatrace

As many companies embrace cloud modernization and begin migrating to the AWS cloud (or continuing to move existing workloads), complexity can introduce uncertainty into the process. What can we move? What will the new architecture be? How can we ensure we see performance gains once migrated? These are the big questions that have slowed, or prevented, many teams from migrating.

In an upcoming partner session at Dynatrace Perform 2024, Mark Jaggers, AWS technical program manager, will detail the key steps of the migration process and showcase where Dynatrace is integral in not only answering these questions, but providing the technical ability and automation to migrate more quickly and confidently.

Session attendees will learn first-hand how Dynatrace natively integrates into the AWS Migration Hub to provide a full topology of on-prem workloads and dependencies in order to generate the ideal cloud-based architecture in the AWS cloud. Additionally, discover how Dynatrace is easily deployed on newly migrated workloads to get instant insights into performance, utilization, security, and efficiency post-migration. Finally, Mark will take attendees a step further to demonstrate how Dynatrace underpins the AWS Well-Architected pillars of cost optimization and operational excellence by helping enterprises to right-size AWS resources with utilization metrics and configuration for continuous efficiency in the cloud.

Learn more about Dynatrace and AWS in the whitepaper, Why modern, well-architected AWS clouds demand AI-powered observability.

Microsoft and Dynatrace solve cloud modernization complexities with generative AI models

In the cacophony of digital noise, where every buzzword promises to revolutionize businesses, how do organizations discern the transformative from the trivial? The struggle to prioritize digital transformation is real, and in this ever-evolving landscape, standing still is not an option. Innovation and cloud modernization aren’t luxuries; they’re the heartbeat of progress.

In this upcoming partner session at Dynatrace Perform 2024, Peter Laudati, Microsoft Cloud solution architect – GPS US, and Jay Gurbani, Dynatrace senior technical partner manager, will share how Microsoft and Dynatrace are helping enterprises solve the complexities introduced by cloud modernization and how organizations can use tools and innovations to unlock the true potential of the digital ecosystem. The session will explore leveraging AI for real-time business decisions and real-world applications.

Learn more about Dynatrace and Microsoft in the whitepaper, Why modern, well-architected Azure clouds demand AI-powered observability.

Enhancing generative AI models in real-world production settings with GCP and Dynatrace

In the ever-evolving landscape of artificial intelligence, the fusion of leading technologies can yield unparalleled results. This partner Perform session will delve into the exploration of Dynatrace, a leading observability and security platform, and Vertex AI Generative AI, a suite of tools within Google Cloud designed for constructing and deploying generative AI models. Attendees can anticipate an examination of the technical intricacies involved in both platforms, offering insights into the possibilities of both systems’ power together. The focus will be on empowering users with the knowledge of how monitoring, analyzing, and optimizing tools can help enhance generative AI models in real-world production settings.

Led by Merlin Yamssi, lead solutions consultant at Google Cloud’s AI/ML CoE partner engineering, and Mike Villiger, senior manager of technical alliances at Dynatrace, this partner session at Perform 2024 will explore Site Reliability Engineering (SRE) and the utilization of the Four Golden Signals for AI Observability. Attendees will gain valuable insight on how Dynatrace observability can complement key Google Cloud AI tools like Duet AI and Vertex AI through its Google Cloud integration and automated discovery processes. Learn more about enhancing system reliability by proactively detecting issues and understanding the capability of Dynatrace and Google Cloud in generative AI models and observability.

Learn more about Dynatrace and GCP from the ebook 5 Key Considerations for Monitoring Google Cloud.

From Las Vegas to the enterprise cloud

The learnings from Perform will be as vast and robust as the enterprise cloud itself and illuminate how Dynatrace is the (not so) secret weapon to accelerating your cloud modernization journey. Beyond the three breakout sessions from our cloud partners, as an attendee, you can also visit them in our expo hall and talk through unique challenges and use cases to further empower you to supercharge your own digital transformation in the cloud.

Don’t miss your chance to learn from and meet with these cloud powerhouses at Dynatrace Perform 2024. Register now to attend in person (or virtually). We’ll see you in Las Vegas and follow you to the cloud!

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